Analysing the top 1,000 twitter accounts associated with technology, Edelman has found that more and more brands – rather than individual people – are using Twitter to influence and engage with their customers and prospects. According to tweetlevel.com analysis, in April, over 40 of the top 200 technology influencers were brands such as Google and Bing up from approximately 25 in January.

Many of these brands saw their influence rise dramatically too. Between January 2010 and April 2010, Ubertwiter jumped from 57th place to 13th, HTC leapt 103places from 194 to 91st and Google rose to 15th from 17th while its rival bing fell from 118th to 186th. The highest ranked brands were Wired (9), TechCrunch (11), Ubertwitter (13) and Google (15). I have included media mastheads that tweet or rather broadcast as brands but classed as personal when it is the individual journalist who tweets as this tends to narrowcast and involve personal as well as news content. These numbers are accurate as of 23 April.

On the whole, relatively few media make it into the top 200 of the 45 brands and only 7 were these kind of branded media. Individual journalists also only form a small proportion of the list where the biggest group remains industry gurus or consultants. Indeed half of the top 10 fall into this category: Jeff Pulver (3), garyvee (5), rww (6), Chris Brogan (7) and Tim O’Reilly (8). Nevertheless as a journalist, Mashable’s Pete Cashmore remains clearly in number one position for influence though very low on engagement.

The blurring of traditional influencer roles is also matched by a blurring of personal and brand tweet names, the clearest example being Scobilizer: guru, journalist, bird or plane? Even analysts are becoming strangely personalized, James Governor’s Monkchips being a great example. Top of the analyst ranking is Jeremiah Owyang who comes in at number 18.

Is this de-personalisation of Twitter polluting the platform and reducing it value as an objective way of looking at the technology landscape? I definitely think a level of authenticity is being lost even when a person was tweeting as an employee there was often a belief in single point of view albeit often either very positive or negative when it came to their employer. However, brand tweeting is by its nature more faceless and an amalgamation of the collective views behind the brand. This is clearly helpful in that it is officially the view of the brand owner but in truth, this also becomes less engaging. I would love to hear your thought.

Following – Twitter lists the number of people each user follows. The tendency for most celebrities is to only follow a few individuals. The more people that someone follows, there is an increased likelihood of them actively participating in conversations with the community instead of simply broadcasting to it. Following ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 30) that was used as part of the algorithm. Note: Twitter opened its API to TweetLevel so that data could be sourced easily and quickly to benefit the user.

Followers – Twitter lists the number of people that follow each user. Like subscribing to a feed, this is a clear indication of ‘popularity’ as it requires someone to actively request participation. Even though TweetLevel has a ranking of people based upon popularity, it is influence, engagement and trust that is more important. Due to the nature of logarithmic ranges, a change in the number of people that follow someone, such as from 500 – 1000, will give a far higher change in score than a move from 180K – 200K. Following ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 30) that was used as part of the algorithm. Note: Twitter opened its API to TweetLevel so that data could be sourced easily and quickly to benefit the user.

Twitter Lists – TweetLevel calculates the number of times someone is included in a twitter list and the corresponding value of that list (as determined by the number of people following it. Ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 30) that was used as part of the algorithm.

Updates – How often does someone update what they are doing. This number is purely objective as it scores someone highly no matter what the content of their post (i.e. how relevant is it). Nevertheless it is assumed that if someone posts frequently but has poor content then their ‘followers’ will decrease. Update ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 30) that was used as part of the algorithm.

Name Pointing – e.g. @name – How many people engage in conversation with a celebrity or point to their name. The clearest way to establish this is to run a search on the number of people who reference @username in a message. This calculation is based upon a one month period combined with a 24 hour period. The number of times this happens is calculated with each range was assigned a number (0 to 30) – again this was then used as part of the algorithm.

Retweets – Has a tweet caused sufficient interest that it is worth re-submitting by others? Despite a great deal of ‘noise’ (i.e. posts that are not relevant or interesting), when someone sees something that is of high interest, their post can be re-tweeted. The clearest way to establish this is to run a search on the number of people who reference RT @username in a message. This calculation is based upon a one month period combined with a 24 hour period. The number of times this happens is calculated with each range was assigned a number (0 to 50) – again this was then used as part of the algorithm.

Twitalyzer – “This is a unique (and online) tool to evaluate the activity of any Twitter user and report on relative influence, signal-to-noise ratio, generosity, velocity, clout, and other useful measures of success in social media.” This 3rd party tool is a useful method to combine automated metrics dependent upon criteria within posts and publicly available numbers. Where tools such as this are available, we incorporate them into the algorithm to achieve a more confident score. Twitalyzer gives users scores from 0 to 100. Ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

Twitalyzer noise to signal ratio – Signal-to-noise ratio is a measure of the tendency for people to pass information, as opposed to anecdote. Signal can be references to other people (defined by the use of “@” followed by text), links to URLs you can visit (defined by the use of “http://” followed by text), hashtags you can explore and participate with (defined by the use of “#” followed by text), retweets of other people, passing along information (defined by the use of “rt”, “r/t/”, “retweet” or “via”). If you take the sum of these four elements and divide that by the number of updates published, you get the “signal to noise” ratio. Twitalyzer gives users scores from 0 to 100. Ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

Twinfluence Rank – Twinfluence is an automated 3rd party tool that uses APIs to measure influence. For example: “Imagine Twitterer1, who has 10,000 followers – most of which are bots and inactives with no followers of their own. Now imagine Twitterer2, who only has 10 followers – but each of them has 5,000 followers. Who has the most real “influence?” Twitterer2, of course.” As with Twitalyzer, this index uses 3rd party tools to add greater confidence in the overall Twitter score. Similar to the other criteria, ranges were determined (i.e. less than 20, less than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

Twitter Grader – Twitter Grader is the final automated tool to add greater confidence to the final index. This site creates a score by evaluating a twitter profile. Similar to the other criteria, ranges were determined (i.e. less than 20, less than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

Involvement Index – The Involvement Index is unique Edelman IP that calculates a score based upon how an individual engages with their community. It is calculated by analysing the content of an individual posts. People who score highest in this category have frequent, relevant, high-quality content that actively involved the twitter community (asking questions, posting links or commenting on discussions) and did not purely consist of broadcasting. Ranges were determined (i.e. less than 20, less than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

Velocity Index – As more people engage on Twitter, it may become harder to keep activity going. The velocity index measures changes on a regular basis and assigns a score based on increased or decreased participation. Ranges were determined (i.e. less than 20, less than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

Weighting – Each specific variable listed above was given a standard score out of 10. Using a weighting scale I varied the importance of the each metric to establish an individual’s total score.

Weighted for Popularity – the key variable is the number of people someone has following them. There are many online tools that show this such as Twitterholic.

Weighted for Engagement – the key variables are an individual’s participation with the Twitter community (as measured by the Involvement Index), with additional emphasis on the frequency of people name pointing an individual (via @username), the numbers of followers and the signal to noise ratio. Other attributes were included in the final score but were given a lower weighting.

Weighted for Influence – the key variables in this instance is a combination of the number and authority of someone’s followers together with the frequency of people name pointing an individual (via @username) and the how many times and individuals posts are re-tweeted. Other attributes were included in the final score but were given a lower weighting.

Weighted for trust – the best measure of trust is whether an in individual is will to ‘trust’ what someone else has said sufficiently that they are also prepared to have what they tweeted associated with them. The key metric in this instance are a combination of retweets and number of followers. Other attributes were included in the final score but were given a lower weighting.

Capturing the data.

The Twitter profiles for this survey were obtained by extensive internet-based research and submissions following a call to action in January to submit technology Tweeters to the list. Obviously, a list like this is dynamic and there are bound to be omissions or the odd errant addition. We encourage you to send any anomalies our way so we can continue to improve the quality of our data in this. Likewise, please send suggestions for inclusion.

Common complaints

Whenever these lists are published, there are several points that always get raised which we will address now…

1. This twitter account is someone who I would regard as being in technology.
The argument as to who is a technologist or not is largely moot. In our opinion if someone is actively talking about technology on Twitter – regardless of whether it’s their day job or not, then that qualifies them for inclusion. We know this will cause a huge amount of disagreement but as an outsider looking in this is the way we see it. This will doubtless lead to issues around non-technology Tweeters (Stephen Fry for example) ranking above more well known technology journalists or brands. But this surely serves to raise the issue of who is considered influential and if it sparks debate and discussion then it’s definitely a good thing.

2. The twitter handle is a brand or written by multiple authors.
We took the decision from the start that technology brands should be included along with individuals. Indeed, it’s the core tenet of our article around brands getting more savvy using the platform. The merits of a single twitter account author allows understanding of the tone of author without having to understand the many personalities that are associated with it but as the results of the survey have shown, brands are moving up the rankings so it would be nonsensical to exclude them and by planning them into context alongside individual Tweeters again debate and discussion is encouraged.

3. Hey – you have forgotten to include this list.
Please let me know the name and we will include it as an edit.

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